2017/08/07 by K. N. R. Surya Vara Prasad, Ekram Hossain, Prasad, K. N. R. Surya Vara +3
Computer Science · Mathematics · #FOS: Computer and information sciences #Information Theory (cs.IT) #Networking and Internet Architecture (cs.NI) #cs.IT #cs.NI #math.IT
paper · pdf · doi:10.48550/arxiv.1708.02279
submitted to IEEE Wireless Communication Letters, July 2017
arxiv created 2017/08/07 · arxiv updated 2017/08/09
We examine the dimensionality of noise-free uplink received signal strength (RSS) data in a distributed multiuser massive multiple-input multiple-output system. Specifically, we apply principal component analysis to the noise-free uplink RSS and observe that it has a low-dimensional principal subspace. We make use of this unique property to propose RecGP - a reconstruction-based Gaussian process regression (GP) method which predicts user locations from uplink RSS data. Considering noise-free RSS for training and noisy test RSS for location prediction, RecGP reconstructs the noisy test RSS from a low- dimensional principal subspace of the noise-free training RSS. The reconstructed RSS is input to a trained GP model for location prediction. Noise reduction facilitated by the reconstruction step allows RecGP to achieve lower prediction error than standard GP methods which directly use the test RSS for location prediction.